What self-hosting AI code review actually costs
The "best self-hosted AI code review tool" question gets searched constantly, and almost everything that ranks is a vendor listicle with no method. One source actually ran a test and published costs. Augment Code put ten open-source review tools against a 450K-file Python, TypeScript, Java and Go monorepo and documented what happened, published 2026-01-16 and updated 2026-08-17. The number worth…
A recent study published on January 16, 2026, and updated in August 2026 examines the costs of self-hosting AI code review tools. Augment Code ran tests on ten open-source review tools against a large Python, TypeScript, Java, and Go monorepo containing 450K files. The study reveals that while the underlying software is free, the costs of self-hosting are not negligible.
The estimate from Augment ranges from $4,100 to $9,100 per month, depending on the team's size and the amount of GPU usage. This cost consists of GPU rates plus 0.25 to 0.5 full-time equivalent (FTE) of maintenance work, based on the US Bureau of Labor Statistics average developer wage. In comparison, commercial per-seat reviewers charge $24 to $30 per month per developer.
The study suggests that self-hosting is not the cheapest option but rather the one that offers privacy and residency advantages. It becomes a capital and staffing cost rather than a subscription line item. While the ten tools tested failed to detect cross-service breaking changes across the four languages, SonarQube Community Build performed best with near-zero false positives, and Semgrep came second in custom rules. However, neither tool is designed for architecture review.
Two critical enterprise control features, audit logging and single sign-on (SSO)/role-based access control (RBAC), are only available with a commercial license. Additionally, there was a configuration issue in PR-Agent that led to silent fallback to hosted models during testing, negating the benefits of a local stack if not addressed.
While the $4,100 bottom of Augment's range assumes modest GPU usage, a team running large models on every pull request (PR) or needing high throughput across multiple repositories might experience higher costs. The study emphasizes that the decision to self-host should be based on a comprehensive model placement decision and a customized price check, rather than solely relying on the published per-seat costs of commercial tools.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
